1. Parallel Image Processing: Active Contour Algorithm
Mohammadhossein Behgam

2. Agenda Overview of Need for parallelism Introduction to Algorithm
Parallelizing
Summery

3. What is the problem? And What is the Solution?
Image Processing applications can be very computationally demanding due to:
Large amount of data
Short response time
Complexity of the algorithm
A wide range of general purpose or custom hardware has been used for image processing.
SIMD: using data parallelism,
Suitable for low level image analysis where each processor performs a uniform set of operations based on the image data matrix in a fixed amount of time
MIMD: using task parallelism and pipelining
Suitable for high level image processing simulations, such as pattern recognition, where each processor is assigned an independent task.

4. Data Parallelism
Task Parallelism

5. Pipeline Parallelism

6. Active Contour Model
Is a framework in computer vision for delineating an object outline from a possibly noisy 2D image.
Is greatly used in applications like object tracking, shape recognition, segmentation, edge detection and stereo matching.
Understood as a special case of the general technique of matching a deformable model to an image by means of energy minimization.

7. Parallel Implementation of Active Contour Algorithm

8. Lowpass Filtering
A low pass filter is the basis for most smoothing methods.
An image is smoothed by decreasing the disparity between pixel values by averaging nearby pixels.

9. Edge Detection
aim at identifying points in a digital image at which the image brightness changes sharply or, more formally, has discontinuities.
Edges are defined by first derivative
Find dx and dy with convolution
Sobel Edge Detection:
Gy =
Gx =

11. Thresholding

12. Distance Transform
Calculates the distance to the closest zero pixel for each pixel of the source image.
Distance metric
Equation
Euclidean
City block
Chess board

13. LU Decomposition System of linear algebraic equations has form
Ax = b
Direct method for solving general linear system is by computing LU factorization
A = LU
System Ax = b then becomes
LUx = b
Solve lower triangular system
Ly = b
by forward-substitution to obtain vector y
Finally, solve upper triangular system
Ux = y
by back-substitution to obtain solution x to original system

15. Parallelizing LU Decomposition

19. Performance

20. Results

21. References Parallel Image Processing, CHARALAMBOS D. STAMOPOULOS
Parallel Image Processing System on a Cluster of Personal Computers, J. Barbosa, J. Tavares and A.J. Padilha
Parallel and Distributed Algorithms for High Speed Image Processing, Jeffrey M. Squyres, Andrew Lumsdaine
Parallel Image Segmentation Using Reduction-Sweeps On Multicore Processors and GPUs, Renato Farias, Ricardo Farias, Ricardo Marroquim
A toolkit for parallel image processing, J. M. Squyres, A. Lumsdaine, R. L. Stevenson

22. Thank you! 
Questions?